Researchers at Disney Research and Boston University have found that a machine learning program can be trained to detect human activity in a video sooner and more accurately than other methods by rewarding the program for gaining confidence in its prediction the longer it observes the activity. It seems intuitive that the program would grow more confident that it is detecting, say, a person changing a tire, the longer it observes the person loosening lugnuts, jacking up the car and subsequently removing the wheel, but that’s not the way most computer models have been trained to detect activity, said Leonid Sigal, senior research scientist at Disney Research. “Most training techniques are happy if the computer model gets 60 percent of the video frames correct, even if the errors occur late…